The identification of model parameters for an unknown or incompletely known process system is important for both control and diagnosis. The more accurately a plant or process can be identified, the better it can be controlled. Estimates of system parameters are an essential aspect of adaptive/predictive control and auto-tuning. In addition, changes in system parameters can be valuable diagnostic indicators. A sudden increase in the delay of a transport process, for example, could imply a blocked pipe.
System identification is the object of extensive research in control theory and a number of techniques have been developed. Most current approaches to system identification can be characterized as hard knowledge approaches derived through extensive mathematical analysis.
A shortcoming of many current system identification approaches is that the assumptions necessary to facilitate the mathematical analysis for a particular application may not be valid for other applications.
A main object of the invention herein is to provide a system identification tool having generality of application. Under this concept, a general purpose technique can be used for a large variety of system identification problems with little or no mathematical effort required. In many applications the short development time that a general purpose technique would allow while still satisfying performance requirements would be a significant advantage.